(532f) Integrated Flexibility and Controllability Analysis for the Continuous Pharmaceutical Manufacturing Process

Batch production mode has been dominant in the pharmaceutical industry during the past several decades. However, it has multiple disadvantages such as large storage volume requirements, strong scale-dependence and potential safety issues. Indeed, the shift from the conventional batch manufacturing to system to the continuous manufacturing method is gaining momentum. The advantages of Continuous Pharmaceutical Manufacturing (CPM) include an increase in efficiency of productivity and economy, maximized automation by the unit operations interconnection, enhanced product quality assurance and safety by a continual automated monitory of process¹.

One of central elements for the successful operation of CPM plants is whether the designed plant can be guaranteed to satisfy the specifications for the entire range of potential uncertain parameter values involved. From the perspective of process system engineering, it is essential to conduct the operability analysis for the CPM to guarantee the desirable dynamic performance under uncertainties. Indeed, flexibility and controllability are two crucial concerns of the operability analysis of chemical plants². Flexibility guarantees feasible steady-state operation over a range of uncertain operating conditions and denotes static resiliency. Controllability reflects the attainable operation of a given process under dynamic operating conditions³. Traditionally, flexibility analysis focuses on processes that are operated at steady state within an uncertainty range, without taking into account the real-time control and dynamic performance of real chemical process. Flexible region may not ensure good performance under dynamic operating conditions.

This paper proposes a framework for the integration of process flexibility and controllability analysis for CPM process. We applied Relative Gain Array (RGA) as an indicator for controllability analysis which is incorporated into the flexibility analysis framework. A mixed integer nonlinear programming (MINLP) model is then formulated. Two cases including complicated micro-reactors to continuously produce Ibuprofen and Artemisinin are carried out to validate the proposed framework.

References

1. Domokos, A.; Nagy, B.; Szilágyi, B.; Marosi, G.; Nagy, Z. K., Integrated Continuous Pharmaceutical Technologies—A Review. Organic Process Research & Development 2021.
2. Bahri, P. A.; Bandoni, J. A.; Romagnoli, J. A., Integrated flexibility and controllability analysis in design of chemical processes. Aiche Journal 1997, 43 (4), 997–1015.
3. Yuan, Z.; Chen, B.; Zhao, J., An overview on controllability analysis of chemical processes. AIChE Journal 2011,57 (5), 1185-1201.